Field
Embodiments described herein generally relate to data analysis systems, and more particularly to building neuro-linguistic models of input data obtained from one or more data sources.
Description of the Related Art
Many currently available surveillance and monitoring systems (e.g., video surveillance systems, SCADA systems, and the like) are trained to observe specific activities and alert an administrator after detecting those activities.
However, such rules-based systems require advance knowledge of what actions and/or objects to observe. The activities may be hard-coded into underlying applications or the system may train itself based on any provided definitions or rules. In other words, unless the underlying code includes descriptions of certain behaviors or rules for generating an alert for a given observation, the system is incapable of recognizing such behaviors. Such a rules-based approach is rigid. That is, unless a given behavior conforms to a predefined rule, an occurrence of the behavior can go undetected by the monitoring system. Even if the system trains itself to identify the behavior, the system requires rules to be defined in advance for what to identify.
In addition, many surveillance systems, e.g., video surveillance systems, require a significant amount of computing resources, including processor power, storage, and bandwidth. For example, typical video surveillance systems require a large amount of computing resources per camera feed because of the typical size of video data. Given the cost of the resources, such systems are difficult to scale.